A method and system for evaluating the reliability of thyristors

Abstract

This invention discloses a method and system for thyristor reliability assessment. First, based on performance degradation data, the initial estimated values ​​of the parameters to be estimated are calculated using the maximum likelihood estimation method. Then, the prior distribution of the parameters to be estimated is determined. Next, based on all experimental data, a Markov chain is constructed using the Gibbs sampling method to obtain a Monte Carlo sample set of the parameters to be estimated. Finally, the values ​​of the parameters to be estimated are estimated using the Monte Carlo sample set, and the reliability of the thyristor is assessed. This invention solves the problem of difficulty in fusing truncated lifetime data in thyristor reliability assessment. Compared with existing methods, this invention has higher data utilization and higher accuracy.

Description

Technical Field

[0001] This invention belongs to the field of high-voltage, high-power thyristor reliability assessment technology, specifically relating to a thyristor reliability assessment method and system. Background Technology

[0002] High-voltage direct current (HVDC) transmission projects are an important method for long-distance, high-power power transmission and are of great significance for the efficient allocation of energy in my country. Thyristors are the core components of converter valves in HVDC transmission projects, and conducting reliability assessments of thyristors used in HVDC transmission is of significant practical importance for the safe and reliable operation of the power grid.

[0003] Thyristors typically have a service life of several decades, and data on thyristor failures under normal operating conditions is limited. To obtain more aging failure data in a shorter time, thyristors are subjected to stress levels higher than normal, a process known as accelerated aging testing. Most existing thyristor reliability assessment methods only analyze the lifespan data from accelerated aging tests, neglecting the performance parameter degradation during the aging process. This results in a significant waste of data.

[0004] The Wiener process is a stochastic process widely used in reliability assessment methods. It is suitable for characterizing the non-monotonic degradation of product performance caused by a large number of random, small losses. Thyristor aging can be viewed as a process of accumulating a large number of random stress damages, and the degradation of thyristor performance parameters exhibits a non-monotonic trend. Therefore, the Wiener process is suitable for describing the thyristor aging process and for reliability assessment.

[0005] The following section introduces the Wiener process and its commonly used definitions in reliability assessment:

[0006] The performance parameter data degradation process of a certain product follows a drift parameter of The diffusion parameter is The Wiener process, denoted as performance parameter degradation data as Then it satisfies the following property:

[0007] (1) The change in performance parameters from time t to time t+Δt2 follows a normal distribution, i.e. ;

[0008] (2) For any Changes in performance parameters and They are independent of each other;

[0009] (3) Y(0) = 0 and Y(t) is continuous at t = 0.

[0010] Let the performance parameter degradation threshold of the product be l, meaning that the product is considered to have failed when the performance parameter first degrades to l. Then, let the product's lifetime be T, and the product's reliability function R(t) be:

[0011] (1)

[0012] in, It is the standard normal distribution function.

[0013] Accelerated aging tests of thyristors inevitably generate failure lifetime data (the lifetime of the thyristor at failure) and truncated lifetime data (the test duration of the thyristor without failure). To fully utilize the test data, it is necessary to fuse these two types of lifetime data with performance parameter degradation data to jointly estimate the Wiener process parameters. and A common method for data fusion is to directly integrate all data to construct a joint likelihood function. However, the likelihood function of truncated lifetime data contains transcendental functions, which are difficult to solve directly using numerical methods. Summary of the Invention

[0014] The technical problem to be solved by this invention is to address the shortcomings of the prior art by providing a thyristor reliability assessment method and system. This method addresses the issue that when commonly used data fusion methods directly fuse all data to construct a joint likelihood function, the likelihood function portion of the truncated data contains transcendental functions, making it difficult to solve directly using numerical methods. This allows for the assessment of Wiener process parameters. and The estimate.

[0015] The present invention adopts the following technical solution:

[0016] A method for evaluating the reliability of thyristors includes the following steps:

[0017] S1. Using performance degradation data, calculate the parameters to be estimated using the maximum likelihood estimation method. initial estimate ;

[0018] S2. Parameters to be estimated based on step S1 initial estimate Determine the parameters to be estimated prior distribution ;

[0019] S3. Prior distribution obtained based on step S2 The parameters to be estimated are obtained using the Gibbs sampling method. Markov chain sample data;

[0020] S4. Calculate the parameters to be estimated based on the Markov chain sample data obtained in step S3. The estimated value and thyristor reliability function R (t), the remaining usable lifetime RUL of the thyristor is calculated.

[0021] Preferably, the parameter to be estimated initial estimate Specifically:

[0022]

[0023]

[0024] in, The number of discrete time points in the aging test sample. For sample i at discrete time points Degradation data obtained from measurements, For each thyristor sample at discrete time points, j=0,1,…,m. For the first i A sample from time... At the time The increase in degradation.

[0025] Preferably, the parameter to be estimated Likelihood function for:

[0026]

[0027] Preferably, the parameter to be estimated is determined. prior distribution Specifically:

[0028] When prior information is insufficient, an uninformative prior distribution is used, and the parameters to be estimated all follow a uniform distribution. The uniform distribution interval encloses the initial estimated value of the parameters to be estimated.

[0029] Preferably, the parameter to be estimated is obtained. The specific Markov chain sample data is as follows:

[0030] Let the number of samples that failed in the experiment be . M The test duration at failure, i.e., the failure life data, are as follows: ;

[0031] Record the number of thyristors that did not fail at the end of the test as . K ,have The truncation lifetime data are as follows: ;

[0032] Let D be the set of performance degradation data, failure lifetime data, and truncation lifetime data;

[0033] Let the failure threshold of the thyristor performance parameters be... l When the thyristor performance parameters first degrade to l When this occurs, it is considered a thyristor failure;

[0034] Let the first k At the start of the next iteration, the model parameter values ​​are The parameter values ​​after iteration are determined sequentially based on the full conditional distribution. , ;

[0035] After several iterations, Markov chain data samples are obtained. The first few non-converged samples are removed, and the remaining samples are used as Monte Carlo samples of the parameters to be estimated, which are then used to estimate the specific values ​​of the parameters.

[0036] Preferably, the full condition distribution is as follows:

[0037] From the full conditional distribution Sample extraction ;

[0038] From the full conditional distribution Sample extraction .

[0039] Preferably, and The formula for calculating the conditional distribution is:

[0040]

[0041]

[0042] in, For failure life data, For truncation lifetime data, For sample i at discrete time points Degradation data obtained from measurements, The time interval is a discrete point in time.

[0043] Preferably, the probability density distribution of the remaining usable lifetime (RUL) for:

[0044]

[0045] in, This refers to the failure threshold of the thyristor's performance parameters. These are the performance parameter values ​​of the thyristor at time t. For variance, For a moment, These are the drift parameters.

[0046] Preferably, the parameter to be estimated The estimated value is:

[0047]

[0048]

[0049] Reliability function R (t) is:

[0050]

[0051] in, For Monte Carlo sample size, The number of non-converged samples to be removed. For the expected drift parameters, For the number of iterations, It is a collection of performance degradation data, failure lifetime data, and truncation lifetime data.

[0052] Secondly, embodiments of the present invention provide a thyristor reliability evaluation system, comprising:

[0053] The input module uses performance degradation data to calculate the parameters to be estimated using the maximum likelihood estimation method. initial estimate ;

[0054] The distribution module, based on the obtained parameters to be estimated initial estimate Determine the parameters to be estimated prior distribution ;

[0055] The sampling module, based on the obtained prior distribution The parameters to be estimated are obtained using the Gibbs sampling method. Markov chain sample data;

[0056] The evaluation module calculates the parameters to be estimated based on the obtained Markov chain sample data. The estimated value and thyristor reliability function R (t), the remaining usable lifetime RUL of the thyristor is calculated.

[0057] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described thyristor reliability assessment method.

[0058] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described thyristor reliability assessment method.

[0059] Compared with the prior art, the present invention has at least the following beneficial effects:

[0060] Existing thyristor reliability assessment methods mostly analyze lifetime data from accelerated aging tests, neglecting the performance parameter degradation during the aging process. This results in significant data waste. Accelerated aging tests of thyristors inevitably generate failure lifetime data (lifetime at which the thyristor fails) and truncated lifetime data (test duration for thyristors that have not failed). To fully utilize the test data, it is necessary to integrate these two types of lifetime data with performance parameter degradation data. The thyristor reliability assessment method proposed in this invention is based on the Wiener process and integrates performance degradation and lifetime data. It constructs a thyristor reliability model by incorporating the physical meaning of the Wiener process. Addressing the lack of degradation data in thyristor tests, a Markov Monte Carlo method is used to jointly model degradation and lifetime data. This solves the problem that common data integration methods, which directly fuse all data to construct a joint likelihood function, contain transcendental functions in the likelihood function portion of the truncated data, making it difficult to solve directly using numerical methods.

[0061] Furthermore, obtaining initial values ​​for the parameters to be estimated provides initial conditions for subsequent iterations.

[0062] Furthermore, the likelihood function is set to obtain a more suitable initial value based on the performance degradation data, making the subsequent iteration process more accurate.

[0063] Furthermore, the prior distribution represents empirical knowledge about things. When there is insufficient or no empirical knowledge about the research subject, a uniform distribution is used for analysis. The uniform distribution interval contains the initial estimated value of the parameter to be estimated, which serves as the mathematical premise for subsequent analysis.

[0064] Furthermore, in order to obtain sample data of the posterior distribution of the parameter to be estimated.

[0065] Furthermore, the conditional distribution is an important step in the Gibbs sampling method to obtain Markov chain data samples, which in turn enables the calculation of the posterior distribution of the parameters.

[0066] It is understandable that the beneficial effects of the second aspect mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0067] In summary, this invention integrates truncated lifetime data, failure lifetime data, and performance degradation data from accelerated aging tests of thyristors to achieve control over Wiener process parameters. and The problem lies in the estimation.

[0068] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0069] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the following description of the relative embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0070] Figure 1 The result of the Markov chain iteration for the parameter sample to be estimated in this invention;

[0071] Figure 2 This is a flowchart of the method of the present invention;

[0072] Figure 3 A schematic diagram of a computer device provided in an embodiment of the present invention;

[0073] Figure 4 This is a block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0074] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0075] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0076] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0077] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0078] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0079] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0080] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0081] This invention provides a method for evaluating the reliability of thyristors. First, based on performance degradation data, the initial estimated value of the parameter to be estimated is calculated using the maximum likelihood estimation method. Then, the prior distribution of the parameter to be estimated is determined. Next, based on all experimental data, a Markov chain is constructed using the Gibbs sampling method to obtain a Monte Carlo sample set of the parameter to be estimated. Finally, the value of the parameter to be estimated is estimated using the Monte Carlo sample set, and the reliability of the thyristor is evaluated. This method solves the technical problem that the likelihood function part of the truncated data contains transcendental functions when the common data joint method directly fuses all data to construct the joint likelihood function, making it difficult to solve directly by numerical methods. This method has a higher data utilization rate.

[0082] The accelerated aging test for thyristors is an electrical durability test. The thyristor in a blocked state is subjected to a voltage stress higher than normal to assess the degradation of its blocking performance. The performance parameter evaluated is the thyristor leakage current.

[0083] In this example, a total of 50 thyristors were tested, with a total test duration of 520 hours. Two thyristors failed during the test. The number of samples for the aging test is denoted as [number missing]. n For each thyristor sample i At discrete time points t j Perform measurements to obtain performance degradation data, i.e. , , ;remember Y For all n The degraded dataset was obtained by observing a number of samples. Y i For the first i A degraded dataset of samples, i.e. , ;remember For the first i A sample from time... At the time The degradation increment; denoted as the number of samples that failed in the experiment. M The test duration at failure, i.e., the failure life data, are as follows: .

[0084] In addition, the number of thyristors that did not fail at the end of the test was recorded as follows: K ,have The truncation lifetime data are as follows: Let D be the set of performance degradation data, failure lifetime data, and truncation lifetime data.

[0085] Please see Figure 2 The present invention provides a thyristor reliability assessment method, comprising the following steps:

[0086] S1. Calculate the parameters to be estimated using the maximum likelihood estimation method based on performance degradation data. initial estimate ;

[0087] Let the number of samples in the aging test be... n For each thyristor sample i At discrete time points t j Perform measurements to obtain performance degradation data, i.e. .remember Y For all n The degraded dataset was obtained by observing a number of samples. Yi For the first i A degraded dataset of samples, i.e. , ;remember For the first i A sample from time... At the time The increase in degradation.

[0088] Based on the properties of the Wiener process, the parameters to be estimated can be obtained. The likelihood function is:

[0089] (1)

[0090] Maximizing the likelihood function yields the parameters to be estimated. initial estimate :

[0091] , (2)

[0092] In this example, by substituting the performance degradation data into equation (2), the parameter to be estimated can be obtained. initial estimate .

[0093] S2. Determine the parameters to be estimated. prior distribution ;

[0094] The prior distribution is determined by prior information such as the degradation law of thyristor electrical parameters. When the prior information is insufficient, an uninformative prior distribution can be used, i.e., the parameter to be estimated is set. It follows a uniform distribution.

[0095] This example chooses an uninformative prior distribution, where all parameters to be estimated follow a uniform distribution. The uniform distribution interval encloses the initial estimated values ​​of the parameters. Generally, the lower limit of the interval is less than 1 / 10 of the initial estimated value, and the upper limit is greater than 10 times the initial estimated value. In this example, the interval is set as follows: , .

[0096] S3. Obtain the parameters to be estimated using the Gibbs sampling method. Markov chain sample data;

[0097] Let the number of samples that failed in the experiment be . M The test duration at failure, i.e., the failure life data, are as follows: .

[0098] In addition, the number of thyristors that did not fail at the end of the test was recorded as follows: K ,have The truncation lifetime data are as follows: Let D be the set of performance degradation data, failure lifetime data, and truncation lifetime data.

[0099] Let the failure threshold of the thyristor performance parameters be... l That is, when the performance parameters of the thyristor first degrade to l When this occurs, the thyristor is considered to be faulty.

[0100] Give parameters and The formula for calculating the conditional distribution is:

[0101] (3)

[0102] (4)

[0103] Let the first k At the start of the next iteration, the model parameter values ​​are The parameter values ​​after iteration are determined sequentially based on the full conditional distribution. , Specifically:

[0104] a) Distribution under full conditions Sample extraction ;

[0105] b) By the full condition distribution Sample extraction ;

[0106] The parameter samples are obtained through several iterations. The first few non-convergent samples are discarded, and the remaining samples are used as Monte Carlo samples for the parameters to be estimated. To ensure the accuracy of the estimation, the number of Monte Carlo samples should be no less than 1000.

[0107] S4. Calculate the parameters to be estimated based on the sample data obtained in step S3. The estimated value and thyristor reliability function R (t), and calculate the remaining usable lifetime RUL.

[0108] Let s be the number of Monte Carlo samples obtained in step S3, and let the parameter to be estimated be... The estimated value is:

[0109] , (5)

[0110] Based on the Monte Carlo sample estimation parameters obtained in step S3 and joint posterior distribution And calculate the reliability function. R (t) is:

[0111] (6)

[0112] Let the performance parameter value of a certain thyristor at a certain moment be... The remaining usable lifetime (RUL) of the thyristor can be predicted, along with its probability density distribution. for:

[0113] (7)

[0114] Steps S3 and S4 can be quickly implemented using WinBUGS software. Alternatively, they can be implemented using MATLAB programming.

[0115] All of the above software is open source.

[0116] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "platform."

[0117] In another embodiment of the present invention, a thyristor reliability evaluation system is provided. This system can be used to implement the above-mentioned thyristor reliability evaluation method. Specifically, the thyristor reliability evaluation system includes an input module, a distribution module, a sampling module, and an evaluation module.

[0118] The input module uses performance degradation data to calculate the parameters to be estimated using the maximum likelihood estimation method. initial estimate ;

[0119] The distribution module, based on the obtained parameters to be estimated initial estimate Determine the parameters to be estimated prior distribution ;

[0120] The sampling module, based on the obtained prior distribution The parameters to be estimated are obtained using the Gibbs sampling method. Markov chain sample data;

[0121] The evaluation module calculates the parameters to be estimated based on the obtained Markov chain sample data. The estimated value and thyristor reliability function R (t), the remaining usable lifetime RUL of the thyristor is calculated.

[0122] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, the computer program including program instructions, and the processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for the operation of a thyristor reliability evaluation method, including:

[0123] Using performance degradation data, the parameters to be estimated are calculated using the maximum likelihood estimation method. initial estimate Based on the obtained parameters to be estimated initial estimate Determine the parameters to be estimated prior distribution Based on the obtained prior distribution The parameters to be estimated are obtained using the Gibbs sampling method. Markov chain sample data; calculate the parameters to be estimated based on the obtained Markov chain sample data. The estimated value and thyristor reliability function R (t), the remaining usable lifetime RUL of the thyristor is calculated.

[0124] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). It should be noted that more specific examples (a non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0125] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0126] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0127] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the thyristor reliability evaluation method in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps:

[0128] Using performance degradation data, the parameters to be estimated are calculated using the maximum likelihood estimation method. initial estimate Based on the obtained parameters to be estimated initial estimate Determine the parameters to be estimated prior distribution Based on the obtained prior distribution The parameters to be estimated are obtained using the Gibbs sampling method. Markov chain sample data; calculate the parameters to be estimated based on the obtained Markov chain sample data. The estimated value and thyristor reliability function R (t), the remaining usable lifetime RUL of the thyristor is calculated.

[0129] Please see Figure 3 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the thyristor reliability evaluation method described in this embodiment. To avoid repetition, details are omitted here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the thyristor reliability evaluation system of this embodiment. To avoid repetition, details are omitted here.

[0130] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 3 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0131] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, CPUs, graphics processing units (GPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, quantum computing-based data processing logic units, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0132] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or RAM of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.

[0133] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0134] Any references to memory, databases, or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0135] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0136] Please see Figure 4 The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0137] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 2 The steps are shown in the figure.

[0138] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0139] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0140] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0141] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0142] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0143] The Markov chain parameter sample generated in this example is as follows: Figure 1 As shown in the figure, the parameter sample values ​​have stabilized after 10 iterations, so the samples from the first 10 iterations are discarded, and the results of subsequent iterations are used as the Monte Carlo sample set for the parameters to be estimated.

[0144] The concept of reliability interval estimation is introduced to assess the sufficiency of data utilization in reliability assessment results. The thyristor reliability function obtained by solving equations (1) and (6) is the expected value of reliability, while the reliability interval estimation describes the probability confidence interval of reliability, i.e.:

[0145]

[0146] in, As the lower confidence limit, For the upper confidence limit, 1- This represents the confidence probability. The narrower the confidence interval, the higher the accuracy of the reliability assessment.

[0147] The method of this invention is used to evaluate and analyze performance degradation data, failure life data, and truncated life data from accelerated aging tests. In addition, for comparison, the commonly used maximum likelihood estimation method is used to evaluate and analyze performance degradation data.

[0148] The model parameter estimation and reliability estimation results are shown in the table below:

[0149]

[0150] The results in the comparison table show that the confidence interval for the reliability estimation of this invention is relatively narrow. At the end of this experiment, a total of two thyristors failed; therefore, the actual reliability of the thyristors is [value missing]. R (520) = 48 / 50 = 0.96, the predicted value of this invention is R (520) = 0.9664, and the maximum likelihood estimation method predicts the value as follows: R (520) = 0.969.

[0151] The above results demonstrate that the present invention can fully integrate and utilize failure lifetime data and truncated lifetime data to improve the accuracy of reliability assessment.

[0152] In summary, the present invention provides a thyristor reliability assessment method and system that solves the problem of difficulty in fusing truncated lifetime data from accelerated aging tests in thyristor reliability assessment based on the Wiener process, thereby fully utilizing test data to improve the accuracy of reliability assessment. Furthermore, the reliability assessment process can be rapidly implemented using WinBUGS software.

[0153] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0154] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0155] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0156] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0157] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0158] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0159] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0160] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0161] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0162] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0163] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for evaluating the reliability of thyristors, characterized in that, Includes the following steps: S1. Using performance degradation data, calculate the Wiener process parameters using the maximum likelihood estimation method. initial estimate ; S2. Parameters to be estimated based on step S1 initial estimate Determine the parameters to be estimated prior distribution ; S3. Prior distribution obtained based on step S2 The parameters to be estimated are obtained using the Gibbs sampling method. The Markov chain sample data is as follows: Let the number of samples that failed in the experiment be . M The test duration at failure, i.e., the failure life data, are as follows: ; Record the number of thyristors that did not fail at the end of the test as . K ,have The truncation lifetime data are as follows: ; Let D be the set of performance degradation data, failure lifetime data, and truncation lifetime data; Let the failure threshold of the thyristor performance parameters be... l When the thyristor performance parameters first degrade to l When this occurs, it is considered a thyristor failure; Let the first k At the start of the next iteration, the model parameter values ​​are The parameter values ​​after iteration are determined sequentially based on the full conditional distribution. , ; After several iterations to obtain Markov chain data samples, the first few non-converged samples are removed, and the remaining samples are used as Monte Carlo samples of the parameters to be estimated, which are then used to estimate the specific values ​​of the parameters. and The formula for calculating the conditional distribution is: in, For failure life data, For truncation lifetime data, For the sample At discrete time points Degradation data obtained from measurements, The time interval is a discrete point in time. S4. Calculate the parameters to be estimated based on the Markov chain sample data obtained in step S3. The estimated value and thyristor reliability function R (t), the remaining usable lifetime RUL of the thyristor is calculated.

2. The thyristor reliability assessment method according to claim 1, characterized in that, Parameters to be estimated initial estimate Specifically: in, This represents the number of samples in the aging test. For sample i at discrete time points Degradation data obtained from measurements, For each thyristor sample at discrete time points, j=0,1,…,m; For the first i A sample from time... At the time The increase in degradation.

3. The thyristor reliability assessment method according to claim 2, characterized in that, Parameters to be estimated Likelihood function for:

4. The thyristor reliability assessment method according to claim 1, characterized in that, Determine the parameters to be estimated prior distribution Specifically: When prior information is insufficient, an uninformative prior distribution is used, and the parameters to be estimated all follow a uniform distribution. The uniform distribution interval encloses the initial estimated value of the parameters to be estimated.

5. The thyristor reliability assessment method according to claim 1, characterized in that, The full conditional distribution is as follows: From the full conditional distribution Sample extraction ; From the full conditional distribution Sample extraction .

6. The thyristor reliability assessment method according to claim 1, characterized in that, Probability density distribution of remaining usable lifetime (RUL) for: in, This refers to the failure threshold of the thyristor's performance parameters. These are the performance parameter values ​​of the thyristor at time t. For variance, For a moment, These are the drift parameters.

7. The thyristor reliability assessment method according to claim 6, characterized in that, Parameters to be estimated The estimated value is: Reliability function R (t) is: in, For Monte Carlo sample size, The number of non-converged samples to be removed. For the expected drift parameters, For the number of iterations, It is a collection of performance degradation data, failure lifetime data, and truncation lifetime data.

8. A thyristor reliability evaluation system, characterized in that, include: The input module uses performance degradation data to calculate the parameters to be estimated using the maximum likelihood estimation method. initial estimate ; The distribution module, based on the obtained parameters to be estimated initial estimate Determine the parameters to be estimated prior distribution ; The sampling module, based on the obtained prior distribution The parameters to be estimated are obtained using the Gibbs sampling method. The Markov chain sample data is as follows: Let the number of samples that failed in the experiment be . M The test duration at failure, i.e., the failure life data, are as follows: ; Record the number of thyristors that did not fail at the end of the test as . K ,have The truncation lifetime data are as follows: ; Let D be the set of performance degradation data, failure lifetime data, and truncation lifetime data; Let the failure threshold of the thyristor performance parameters be... l When the thyristor performance parameters first degrade to l When this occurs, it is considered a thyristor failure; Let the first k At the start of the next iteration, the model parameter values ​​are The parameter values ​​after iteration are determined sequentially based on the full conditional distribution. , ; After several iterations to obtain Markov chain data samples, the first few non-converged samples are removed, and the remaining samples are used as Monte Carlo samples of the parameters to be estimated, which are then used to estimate the specific values ​​of the parameters. and The formula for calculating the conditional distribution is: in, For failure life data, For truncation lifetime data, For the sample At discrete time points Degradation data obtained from measurements, The time interval is a discrete point in time. The evaluation module calculates the parameters to be estimated based on the obtained Markov chain sample data. The estimated value and thyristor reliability function R (t), the remaining usable lifetime RUL of the thyristor is calculated.

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